class_docstr: MRC Sample class to hold the mrc data info and provide atomic operations.
docstr: Convert data dict which contains essential information to MRCCnSample.
  
  :param dict data: the dict obj that contains dict info
docstr_tok: ["Convert","data","dict","which","contains","essential","information","to","MRCCnSample",".",":","param","dict","data",":","the","dict","obj","that","contains","dict","info"]
code: def load(self, data):
      r"""
      Convert data dict which contains essential information to MRCCnSample.
  
      :param dict data: the dict obj that contains dict info
  
      """
      self.context = CnTextField(data["context"])
      self.question = CnTextField(data["question"])
      self.title = Field(data["title"])
      if isinstance(data["is_impossible"], bool):
          self.is_impossible = data["is_impossible"]
      elif isinstance(data["is_impossible"], str):
          self.is_impossible = (
              True if data["is_impossible"] == "True" else False
          )
      self.is_impossible = data["is_impossible"]
      self.answers = []
  
      if self.is_impossible:
          self.answers = []
      else:
          spans = self.convert_idx(data["context"], self.context.tokens)
          self.load_answers(data["answers"], spans)
  
          if not self.is_legal():
              raise ValueError(
                  "Data sample {0} is not legal, "
                  "Answer spans mismatch answer text.".format(data)
              )
  
imports: from .cnsample import CnSample
  from ..field import Field, CnTextField
  from ....common.utils.list_op import normalize_scope
  from ....common.preprocess.nltk_res_load import ModelManager
  from ....common.settings import *
  from nltk.stem.lancaster import LancasterStemmer
  from copy import deepcopy
label_desc: Use image node_id 9 for calling the MRCCnSample obj's underlying member method code with example usage: obj.load(data) without return types